Note

Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder

A demo of the mean-shift clustering algorithm#

Reference:

Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward feature space analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importnumpyasnp
fromsklearn.clusterimport MeanShift , estimate_bandwidth
fromsklearn.datasetsimport make_blobs

Generate sample data#

centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs (n_samples=10000, centers=centers, cluster_std=0.6)

Compute clustering with MeanShift#

# The following bandwidth can be automatically detected using
bandwidth = estimate_bandwidth (X, quantile=0.2, n_samples=500)
ms = MeanShift (bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique (labels)
n_clusters_ = len(labels_unique)
print("number of estimated clusters : %d" % n_clusters_)

number of estimated clusters : 3

Plot result#

importmatplotlib.pyplotasplt
plt.figure (1)
plt.clf ()
colors = ["#dede00", "#377eb8", "#f781bf"]
markers = ["x", "o", "^"]
for k, col in zip(range(n_clusters_), colors):
 my_members = labels == k
 cluster_center = cluster_centers[k]
 plt.plot (X[my_members, 0], X[my_members, 1], markers[k], color=col)
 plt.plot (
 cluster_center[0],
 cluster_center[1],
 markers[k],
 markerfacecolor=col,
 markeredgecolor="k",
 markersize=14,
 )
plt.title ("Estimated number of clusters: %d" % n_clusters_)
plt.show ()